Grammar: how to tame the unruly beast

Pearson Languages
A young child sat at a desk in a classroom writing

“Grammar, which knows how to control even kings” - Molière

When you think of grammar, “rule” is probably the first word that pops into your mind. Certainly the traditional view of grammar is that it’s about the “rules of language”. Indeed, not so long ago, teaching a language meant just teaching grammatical rules, plus perhaps a few vocabulary lists. However, I’m going to suggest that there’s actually no such thing as a grammatical rule.

To show you what I mean, let’s take the comparative of adjectives: “bigger”, “smaller”, “more useful”, “more interesting”, etc. We might start with a simple rule: for adjectives with one syllable, add -er, and for adjectives with two or more syllables, use more + adjective.

But this doesn’t quite work: yes, we say “more useful”, but we also say “cleverer”, and “prettier”. OK then, suppose we modify the rule. Let’s also say that for two-syllable adjectives ending in -y or -er you add -er.

Unfortunately, this doesn’t quite work either: we do say “cleverer”, but we also say “more sober” and “more proper”. And there are problems with some of the one-syllable adjectives too: we say “more real” and “more whole” rather than “realer” or “wholer”. If we modify the rule to fit these exceptions, it will be half a page long, and anyway, if we keep looking we’ll find yet more exceptions. This happens repeatedly in English grammar. Very often, rules seem so full of exceptions that they’re just not all that helpful.

And there’s another big problem with the “rule approach”: it doesn’t tell you what the structure is actually used for, even with something as obvious as the comparative of adjectives. You might assume that it’s used for comparing things: “My house is smaller than Mary’s”; “John is more attractive than Stephen”. But look at this: “The harder you work, the more money you make.” Or this: “London is getting more and more crowded.” Both sentences use comparative adjectives, but they’re not directly comparing two things.

What we’re actually looking at here is not a rule but several overlapping patterns, or paradigms to use the correct technical term:

  1. adjective + -er + than
  2. more + adjective + than
  3. parallel comparative adjectives: the + comparative adjective 1 … the + comparative adjective 2
  4. repeated comparative adjective: adjective + -er + and + adjective + -er/more and more + adjective

This picture is more accurate, but it looks abstract and technical. It’s a long way from what we actually teach these days and the way we teach it, which tends to be organized around learning objectives and measurable outcomes, such as: “By the end of this lesson (or module) my students should be able to compare their own possessions with someone else’s possessions”. So we’re not teaching our students to memorize a rule or even to manipulate a pattern; we’re teaching them to actually do something in the real world. And, of course, we’re teaching it at a level appropriate for the student’s level.

So, to come back to grammar, once we’ve established our overall lesson or module objective, here are some of the things we’re going to need to know.

  • What grammatical forms (patterns) can be used to express this objective?
  • Which ones are appropriate for the level of my students? Are there some that they should already know, or should I teach them in this lesson?
  • What do the forms look like in practice? What would be some good examples?

Existing grammar textbooks generally don’t provide all this information; in particular, they’re very vague about level. Often they don’t even put grammar structures into specific CEFR levels but into a range, e.g. A1/A2 or A2/B1, and none fully integrates grammar with overall learning objectives.

At Pearson, we’ve set ourselves the goal of addressing these issues by developing a new type of grammar resource for English teachers and learners that:

  • Is based on the Global Scale of English with its precise gradation of developing learner proficiency
  • Is built on the Council of Europe language syllabuses, linking grammar to CEFR level and to language functions
  • Uses international teams of language experts to review the structures and assess their levels

We include grammar in the GSE Teacher Toolkit, and you can use it to:

  • Search for grammar structures either by GSE or CEFR level
  • Search for grammar structures by keyword or grammatical category/part of speech
  • Find out at which level a given grammar structure should be taught
  • Find out which grammar structures support a given learning objective
  • Find out which learning objectives are related to a given grammar structure
  • Get examples for any given grammar structure
  • Get free teaching materials for many of the grammar structures

Think of it as an open-access resource for anyone teaching English and designing a curriculum.

More blogs from Pearson

  • A group of business people sat together at a desk

    Beyond borders: Harnessing the power of English language skills for a global competitive edge

    By Samantha Yates
    Reading time: 7 minutes

    How does increasing English proficiency drive international growth? Read on to find out how future-focused business leaders are gaining a competitive edge globally by investing in English language training.

    The link between English language proficiency and global business growth is indisputable, and this presents leaders with an exciting opportunity to gain a competitive advantage.

  • A girl sat at a desk with a laptop and notepad studying and taking notes

    AI scoring vs human scoring for language tests: What's the difference?

    By Charlotte Guest
    Reading time: 6 minutes

    When entering the world of language proficiency tests, test takers are often faced with a dilemma: Should they opt for tests scored by humans or those assessed by artificial intelligence (AI)? The choice might seem trivial at first, but understanding the differences between AI scoring and human language test scoring can significantly impact preparation strategy and, ultimately, determine test outcomes.

    The human touch in language proficiency testing and scoring

    Historically, language tests have been scored by human assessors. This method leverages the nuanced understanding that humans have of language, including idiomatic expressions, cultural references, and the subtleties of tone and even writing style, akin to the capabilities of the human brain. Human scorers can appreciate the creative and original use of language, potentially rewarding test takers for flair and originality in their answers. Scorers are particularly effective at evaluating progress or achievement tests, which are designed to assess a student's language knowledge and progress after completing a particular chapter, unit, or at the end of a course, reflecting how well the language tester is performing in their language learning studies.

    One significant difference between human and AI scoring is how they handle context. Human scorers can understand the significance and implications of a particular word or phrase in a given context, while AI algorithms rely on predetermined rules and datasets.

    The adaptability and learning capabilities of human brains contribute significantly to the effectiveness of scoring in language tests, mirroring how these brains adjust and learn from new information.

    Advantages:

    • Nuanced understanding: Human scorers are adept at interpreting the complexities and nuances of language that AI might miss.
    • Contextual flexibility: Humans can consider context beyond the written or spoken word, understanding cultural and situational implications.

    Disadvantages:

    • Subjectivity and inconsistency: Despite rigorous training, human-based scoring can introduce a level of subjectivity and variability, potentially affecting the fairness and reliability of scores.
    • Time and resource intensive: Human-based scoring is labor-intensive and time-consuming, often resulting in longer waiting times for results.
    • Human bias: Assessors, despite being highly trained and experienced, bring their own perspectives, preferences and preconceptions into the grading process. This can lead to variability in scoring, where two equally competent test takers might receive different scores based on the scorer's subjective judgment.

    The rise of AI in language test scoring

    With advancements in technology, AI-based scoring systems have started to play a significant role in language assessment. These systems utilize algorithms and natural language processing (NLP) techniques to evaluate test responses. AI scoring promises objectivity and efficiency, offering a standardized way to assess language and proficiency level.

    Advantages:

    • Consistency: AI scoring systems provide a consistent scoring method, applying the same criteria across all test takers, thereby reducing the potential for bias.
    • Speed: AI can process and score tests much faster than human scorers can, leading to quicker results turnaround.
    • Great for more nervous testers: Not everyone likes having to take a test in front of a person, so AI removes that extra stress.

    Disadvantages:

    • Lack of nuance recognition: AI may not fully understand subtle nuances, creativity, or complex structures in language the way a human scorer can.
    • Dependence on data: The effectiveness of AI scoring is heavily reliant on the data it has been trained on, which can limit its ability to interpret less common responses accurately.

    Making the choice

    When deciding between tests scored by humans or AI, consider the following factors:

    • Your strengths: If you have a creative flair and excel at expressing original thoughts, human-scored tests might appreciate your unique approach more. Conversely, if you excel in structured language use and clear, concise expression, AI-scored tests could work to your advantage.
    • Your goals: Consider why you're taking the test. Some organizations might prefer one scoring method over the other, so it's worth investigating their preferences.
    • Preparation time: If you're on a tight schedule, the quicker turnaround time of AI-scored tests might be beneficial.

    Ultimately, both scoring methods aim to measure and assess language proficiency accurately. The key is understanding how each approach aligns with your personal strengths and goals.

    The bias factor in language testing

    An often-discussed concern in both AI and human language test scoring is the issue of bias. With AI scoring, biases can be ingrained in the algorithms due to the data they are trained on, but if the system is well designed, bias can be removed and provide fairer scoring.

    Conversely speaking, human scorers, despite their best efforts to remain objective, bring their own subconscious biases to the evaluation process. These biases might be related to a test taker's accent, dialect, or even the content of their responses, which could subtly influence the scorer's perceptions and judgments. Efforts are continually made to mitigate these biases in both approaches to ensure a fair and equitable assessment for all test takers.

    Preparing for success in foreign language proficiency tests

    Regardless of the scoring method, thorough preparation remains, of course, crucial. Familiarize yourself with the test format, practice under timed conditions, and seek feedback on your performance, whether from teachers, peers, or through self-assessment tools.

    The distinctions between AI scoring and human in language tests continue to blur, with many exams now incorporating a mix of both to have students leverage their respective strengths. Understanding and interpreting written language is essential in preparing for language proficiency tests, especially for reading tests. By understanding these differences, test takers can better prepare for their exams, setting themselves up for the best possible outcome.

    Will AI replace human-marked tests?

    The question of whether AI will replace markers in language tests is complex and multifaceted. On one hand, the efficiency, consistency and scalability of AI scoring systems present a compelling case for their increased utilization. These systems can process vast numbers of tests in a fraction of the time it takes markers, providing quick feedback that is invaluable in educational settings. On the other hand, the nuanced understanding, contextual knowledge, flexibility, and ability to appreciate the subtleties of language that human markers bring to the table are qualities that AI has yet to fully replicate.

    Both AI and human-based scoring aim to accurately assess language proficiency levels, such as those defined by the Common European Framework of Reference for Languages or the Global Scale of English, where a level like C2 or 85-90 indicates that a student can understand virtually everything, master the foreign language perfectly, and potentially have superior knowledge compared to a native speaker.

    The integration of AI in language testing is less about replacement and more about complementing and enhancing the existing processes. AI can handle the objective, clear-cut aspects of language testing, freeing markers to focus on the more subjective, nuanced responses that require a human touch. This hybrid approach could lead to a more robust, efficient and fair assessment system, leveraging the strengths of both humans and AI.

    Future developments in AI technology and machine learning may narrow the gap between AI and human grading capabilities. However, the ethical considerations, such as ensuring fairness and addressing bias, along with the desire to maintain a human element in education, suggest that a balanced approach will persist. In conclusion, while AI will increasingly play a significant role in language testing, it is unlikely to completely replace markers. Instead, the future lies in finding the optimal synergy between technological advancements and human judgment to enhance the fairness, accuracy and efficiency of language proficiency assessments.

    Tests to let your language skills shine through

    Explore Pearson's innovative language testing solutions today and discover how we are blending the best of AI technology and our own expertise to offer you reliable, fair and efficient language proficiency assessments. We are committed to offering reliable and credible proficiency tests, ensuring that our certifications are recognized for job applications, university admissions, citizenship applications, and by employers worldwide. Whether you're gearing up for academic, professional, or personal success, our tests are designed to meet your diverse needs and help unlock your full potential.

    Take the next step in your language learning journey with Pearson and experience the difference that a meticulously crafted test can make.

  • Woman standing outside with a coffee and headphones

    Using language learning as a form of self-care for wellbeing

    By Charlotte Guest
    Reading time: 6.5 minuts

    In today’s fast-paced world, finding time for self-care is more important than ever. Among a range of traditional self-care practices, learning a language emerges as an unexpected but incredibly rewarding approach. Learning a foreign language is a key aspect of personal development and can help your mental health, offering benefits like improved career opportunities, enhanced creativity, and the ability to connect with people from diverse cultures.